
import pandas as pd
import numpy as np                     # For mathematical calculations
import seaborn as sns                  # For data visualization
import matplotlib.pyplot as plt 
import seaborn as sn                   # For plotting graphs
import io
%matplotlib inline
import warnings                        # To ignore any warnings
warnings.filterwarnings("ignore")


filepath2 = r"C:\Users\91623\OneDrive\Desktop\projects\COUSTOMER REALATIONPROJECT\data1.txt"
df1= pd.read_csv(filepath2,delimiter=';')
print(df1)

         ID  Year_Birth   Education Marital_Status   Income  Kidhome  \
0      5524        1957  Graduation         Single  58138.0        0   
1      2174        1954  Graduation         Single  46344.0        1   
2      4141        1965  Graduation       Together  71613.0        0   
3      6182        1984  Graduation       Together  26646.0        1   
4      5324        1981         PhD        Married  58293.0        1   
...     ...         ...         ...            ...      ...      ...   
2235  10870        1967  Graduation        Married  61223.0        0   
2236   4001        1946         PhD       Together  64014.0        2   
2237   7270        1981  Graduation       Divorced  56981.0        0   
2238   8235        1956      Master       Together  69245.0        0   
2239   9405        1954         PhD        Married  52869.0        1   

      Teenhome Dt_Customer  Recency  MntWines  ...  NumWebVisitsMonth  \
0            0  2012-09-04       58       635  ...                  7   
1            1  2014-03-08       38        11  ...                  5   
2            0  2013-08-21       26       426  ...                  4   
3            0  2014-02-10       26        11  ...                  6   
4            0  2014-01-19       94       173  ...                  5   
...        ...         ...      ...       ...  ...                ...   
2235         1  2013-06-13       46       709  ...                  5   
2236         1  2014-06-10       56       406  ...                  7   
2237         0  2014-01-25       91       908  ...                  6   
2238         1  2014-01-24        8       428  ...                  3   
2239         1  2012-10-15       40        84  ...                  7   

      AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  \
0                0             0             0             0             0   
1                0             0             0             0             0   
2                0             0             0             0             0   
3                0             0             0             0             0   
4                0             0             0             0             0   
...            ...           ...           ...           ...           ...   
2235             0             0             0             0             0   
2236             0             0             0             1             0   
2237             0             1             0             0             0   
2238             0             0             0             0             0   
2239             0             0             0             0             0   

      Complain  Z_CostContact  Z_Revenue  Response  
0            0              3         11         1  
1            0              3         11         0  
2            0              3         11         0  
3            0              3         11         0  
4            0              3         11         0  
...        ...            ...        ...       ...  
2235         0              3         11         0  
2236         0              3         11         0  
2237         0              3         11         0  
2238         0              3         11         0  
2239         0              3         11         1  

[2240 rows x 29 columns]


df1.head()


df1.describe()


df1.shape

(2240, 29)


df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 29 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   ID                   2240 non-null   int64  
 1   Year_Birth           2240 non-null   int64  
 2   Education            2240 non-null   object 
 3   Marital_Status       2240 non-null   object 
 4   Income               2216 non-null   float64
 5   Kidhome              2240 non-null   int64  
 6   Teenhome             2240 non-null   int64  
 7   Dt_Customer          2240 non-null   object 
 8   Recency              2240 non-null   int64  
 9   MntWines             2240 non-null   int64  
 10  MntFruits            2240 non-null   int64  
 11  MntMeatProducts      2240 non-null   int64  
 12  MntFishProducts      2240 non-null   int64  
 13  MntSweetProducts     2240 non-null   int64  
 14  MntGoldProds         2240 non-null   int64  
 15  NumDealsPurchases    2240 non-null   int64  
 16  NumWebPurchases      2240 non-null   int64  
 17  NumCatalogPurchases  2240 non-null   int64  
 18  NumStorePurchases    2240 non-null   int64  
 19  NumWebVisitsMonth    2240 non-null   int64  
 20  AcceptedCmp3         2240 non-null   int64  
 21  AcceptedCmp4         2240 non-null   int64  
 22  AcceptedCmp5         2240 non-null   int64  
 23  AcceptedCmp1         2240 non-null   int64  
 24  AcceptedCmp2         2240 non-null   int64  
 25  Complain             2240 non-null   int64  
 26  Z_CostContact        2240 non-null   int64  
 27  Z_Revenue            2240 non-null   int64  
 28  Response             2240 non-null   int64  
dtypes: float64(1), int64(25), object(3)
memory usage: 507.6+ KB


df=df1.drop('ID',axis=1)


df.head()


unique = [feature for feature in df.columns if len(df[feature].unique())>0 and len(df[feature].unique())<100]
for feature in unique:
    print("{} has {} unique values : {} {}".format(feature,len(df[feature].unique()),df[feature].unique(),"\n"))

Year_Birth has 59 unique values : [1957 1954 1965 1984 1981 1967 1971 1985 1974 1950 1983 1976 1959 1952
 1987 1946 1980 1949 1982 1979 1951 1969 1986 1989 1963 1970 1973 1943
 1975 1996 1968 1964 1977 1978 1955 1966 1988 1948 1958 1972 1960 1945
 1991 1962 1953 1961 1956 1992 1900 1893 1990 1947 1899 1993 1994 1941
 1944 1995 1940] 

Education has 5 unique values : ['Graduation' 'PhD' 'Master' 'Basic' '2n Cycle'] 

Marital_Status has 8 unique values : ['Single' 'Together' 'Married' 'Divorced' 'Widow' 'Alone' 'Absurd' 'YOLO'] 

Kidhome has 3 unique values : [0 1 2] 

Teenhome has 3 unique values : [0 1 2] 

NumDealsPurchases has 15 unique values : [ 3  2  1  5  4 15  7  0  6  9 12  8 10 13 11] 

NumWebPurchases has 15 unique values : [ 8  1  2  5  6  7  4  3 11  0 27 10  9 23 25] 

NumCatalogPurchases has 14 unique values : [10  1  2  0  3  4  6 28  9  5  8  7 11 22] 

NumStorePurchases has 14 unique values : [ 4  2 10  6  7  0  3  8  5 12  9 13 11  1] 

NumWebVisitsMonth has 16 unique values : [ 7  5  4  6  8  9 20  2  3  1 10  0 14 19 17 13] 

AcceptedCmp3 has 2 unique values : [0 1] 

AcceptedCmp4 has 2 unique values : [0 1] 

AcceptedCmp5 has 2 unique values : [0 1] 

AcceptedCmp1 has 2 unique values : [0 1] 

AcceptedCmp2 has 2 unique values : [0 1] 

Complain has 2 unique values : [0 1] 

Z_CostContact has 1 unique values : [3] 

Z_Revenue has 1 unique values : [11] 

Response has 2 unique values : [1 0]


df.columns = df.columns.str.lower()


df.isnull().sum()

year_birth              0
education               0
marital_status          0
income                 24
kidhome                 0
teenhome                0
dt_customer             0
recency                 0
mntwines                0
mntfruits               0
mntmeatproducts         0
mntfishproducts         0
mntsweetproducts        0
mntgoldprods            0
numdealspurchases       0
numwebpurchases         0
numcatalogpurchases     0
numstorepurchases       0
numwebvisitsmonth       0
acceptedcmp3            0
acceptedcmp4            0
acceptedcmp5            0
acceptedcmp1            0
acceptedcmp2            0
complain                0
z_costcontact           0
z_revenue               0
response                0
dtype: int64


df= df.dropna()
df.isnull().sum()

year_birth             0
education              0
marital_status         0
income                 0
kidhome                0
teenhome               0
dt_customer            0
recency                0
mntwines               0
mntfruits              0
mntmeatproducts        0
mntfishproducts        0
mntsweetproducts       0
mntgoldprods           0
numdealspurchases      0
numwebpurchases        0
numcatalogpurchases    0
numstorepurchases      0
numwebvisitsmonth      0
acceptedcmp3           0
acceptedcmp4           0
acceptedcmp5           0
acceptedcmp1           0
acceptedcmp2           0
complain               0
z_costcontact          0
z_revenue              0
response               0
dtype: int64


df.shape

(2216, 28)


df['total_kids'] = df['kidhome']+df['teenhome']
df.head()


sns.countplot(df['total_kids'],palette='dark:r');


df['total_accep'] = df['acceptedcmp1'] + df['acceptedcmp2']+df['acceptedcmp3'] + df['acceptedcmp4']	+df['acceptedcmp5']	+df['response']

df['total_purchase'] = df['numdealspurchases']+df['numwebpurchases']+df['numcatalogpurchases']+df['numstorepurchases']

df.head()


df.drop(['kidhome','teenhome','numdealspurchases','numwebpurchases','numcatalogpurchases','numstorepurchases','numwebvisitsmonth','acceptedcmp3',
         'acceptedcmp4','acceptedcmp5','acceptedcmp1','acceptedcmp2','complain','z_costcontact','z_revenue','response'],axis=1,inplace=True)


df.head()


df['age'] = 2014-df['year_birth']
df.head()


df['marital_status'] = df['marital_status'].replace({"Together":"Married", "Absurd":"Single", "Widow":"Single", "YOLO":"Single", "Divorced":"Single", "Alone":"Single",})
df


print("marital_status :",df.marital_status.unique())

marital_status : ['Single' 'Married']


df['education'] = df['education'].replace({"Basic":"Undergraduate","2n Cycle":"Undergraduate", "Graduation":"Graduate", "Master":"Postgraduate", "PhD":"Postgraduate"})


print("education:",df.education.unique())

education: ['Graduate' 'Postgraduate' 'Undergraduate']


df=df.drop("year_birth",axis=1)


df['isparent'] = df['total_kids']>0
df.head()


df["spent"] = df["mntwines"]+ df["mntfruits"]+ df["mntmeatproducts"]+ df["mntfishproducts"]+ df["mntsweetproducts"]+df["mntgoldprods"]


df.head()


df1=df


df1= df1.drop(["mntwines","mntfruits","mntmeatproducts","mntfishproducts","mntsweetproducts","mntgoldprods"],axis=1)

df1


df1= df1.drop(["dt_customer","recency"],axis=1)

df1


df["dt_customer"] = pd.to_datetime(df["dt_customer"]).dt.date
print("The newest date is:",max(df["dt_customer"]))
print("The oldest date is:",min(df["dt_customer"]))



import requests
from datetime import date
days = []
d1 = max(df['dt_customer'])
for i in df['dt_customer']:
    
    delta = d1 - i
    days.append(delta.days)
df["total_days"] = days
df["total_days"] = pd.to_numeric(df["total_days"], errors="coerce")

The newest date is: 2014-06-29
The oldest date is: 2012-07-30


from pandas_profiling import ProfileReport
cra = ProfileReport(df1)
cra.to_file(output_file="cra_report.html")


df.head()


numerical_features = [feature for feature in df.columns if df[feature].dtype != "O"]
print("we have {} numerical features out of {}".format(len(numerical_features),len(df.columns)))

we have 15 numerical features out of 18


discrete_features = [feature for feature in numerical_features if len(df[feature].unique())<25]
print("we have {} discrete features out of {} total features".format(len(discrete_features),len(df.columns)))

we have 3 discrete features out of 18 total features


df[discrete_features].head()


fig,ax = plt.subplots(4,4,figsize=(20,15))
for variable, subplot in zip(discrete_features,ax.flatten()):
    sns.countplot(df[variable],ax=subplot,palette="winter")


continuos_features = [feature for feature in numerical_features if feature not in discrete_features]
print("we have {} continuos features out of {} total features".format(len(continuos_features),len(df.columns)))

we have 12 continuos features out of 18 total features


fig,ax = plt.subplots(3,3,figsize=(20,12))
for variable, subplot in zip(continuos_features,ax.flatten()):
    sns.histplot(df[variable],ax=subplot)


plt.figure(figsize=(20,5))
plt.subplot(1,2,1)
sns.histplot(df['age'],color='red')
plt.title('Distribution of age')

plt.subplot(1,2,2)
sns.histplot(df['spent'],color='blue',bins=10)
plt.title('Distribution of total_amount_spent');


categorical_features = [feature for feature in df.columns if df[feature].dtype == "O"]
print("we have {} continuos features out of {} features".format(len(categorical_features),len(df.columns)))

we have 3 continuos features out of 18 features


df[categorical_features].head()


from datetime import date
days = []
d1 = max(df['dt_customer'])
for i in df['dt_customer']:
    delta = d1 - i
    days.append(delta.days)
df["total_days"] = days
df["total_days"] = pd.to_numeric(df["total_days"], errors="coerce")


plt.figure(figsize=(15,5))
plt.subplot(1,2,1)
sns.countplot(df['marital_status'],palette ='spring')
plt.title('Distribution of marital status')

plt.subplot(1,2,2)
sns.countplot(df['education'],palette ='winter')
plt.title('Distribution of Education');


df[categorical_features].head()


plt.figure(figsize=(14,5))


sns.countplot(df1['isparent'],palette='ocean_r')
plt.title('distribution of is parent');


df['family'] = df['marital_status'].replace({'Single':1,"Married":2}) + df['total_kids']


sns.countplot(df['family'],palette='spring')
plt.title('distribution of family')

Text(0.5, 1.0, 'distribution of family')


sns.scatterplot(data =df['age'])
plt.title('scatterplot of age');


df[df['age']>90]

df1


df= df.drop(["dt_customer"],axis=1)


sns.boxplot(df['age'])
plt.title("Age- Before outlier removal");


df = df.drop(df[df['age']>90].index)


sns.boxplot(df['age'],color='g')
plt.title('Age- After outlier removal')

Text(0.5, 1.0, 'Age- After outlier removal')


sns.scatterplot(data = df['income'])
plt.title('scatterplot of income');


sns.boxplot(df['income'])
plt.title("income- Before outlier removal");


df = df.drop(df[df['income']>600000].index)


sns.boxplot(df['income'])

<AxesSubplot:xlabel='income'>


df1 = df1.drop(df1[df1['income']>600000].index)


df1 = df1.drop(df1[df1['age']>90].index)


df1.head()


sns.scatterplot(data = df1['spent'])
plt.title('scatterplot of spent');

df1


sns.histplot(df1['income'],color='red',label='income')
sns.histplot(df1['spent'],label='spent')
plt.legend()
plt.title('Histogram of income and spent')

Text(0.5, 1.0, 'Histogram of income and spent')


import plotly.express as px
import plotly.graph_objects as go
fig = px.scatter(df1, x="total_purchase", y="income", color="marital_status",
    size="spent", size_max=45, log_x=True)

fig.update_layout(legend=dict(
    yanchor="top",
    y=0.99,
    xanchor="left",
    x=0.01
))

fig.show()


fig = px.sunburst(df1, path=['education', 'marital_status','total_purchase'], branchvalues='total',color='total_purchase',template='plotly',title='total purchase')
fig.show()


corrmat=df.corr()
top_corr=corrmat.index
plt.figure(figsize=(20,20))
g= sns.heatmap(df[top_corr].corr(),annot= True,cmap="RdYlGn")


corrmat=df1.corr()
top_corr=corrmat.index
plt.figure(figsize=(20,20))
g= sns.heatmap(df[top_corr].corr(),annot= True,cmap="RdYlGn")


pos_corrmat = df.corr()
filtereddf = pos_corrmat[((pos_corrmat >= .5) & (pos_corrmat !=1.000))]
plt.figure(figsize=(15,5))
sns.heatmap(filtereddf, annot=True, cmap="Reds")
plt.title('Positive correlation matrix')
plt.show()


neg_corrmat = df.corr()
filtereddf = neg_corrmat[((neg_corrmat <-.3) & (neg_corrmat !=1.000))]
plt.figure(figsize=(15,5))
sns.heatmap(filtereddf, annot=True, cmap="Reds")
plt.title('Negative correlation matrix')
plt.show()


pos_corrmat = df1.corr()
filtereddf = pos_corrmat[((pos_corrmat >= .5) & (pos_corrmat !=1.000))]
plt.figure(figsize=(15,5))
sns.heatmap(filtereddf, annot=True, cmap="Reds")
plt.title('Positive correlation matrix')
plt.show()


neg_corrmat = df1.corr()
filtereddf = neg_corrmat[((neg_corrmat <-.3) & (neg_corrmat !=1.000))]
plt.figure(figsize=(15,5))
sns.heatmap(filtereddf, annot=True, cmap="Reds")
plt.title('Negative correlation matrix')
plt.show()


sns.pairplot(df,hue='income')

<seaborn.axisgrid.PairGrid at 0x2508b3651c0>


sns.pairplot(df1,hue='income')

<seaborn.axisgrid.PairGrid at 0x25096aedf70>


df.loc[(df['age'] >= 13) & (df['age'] <= 19), 'ageGroup'] = 'Teen'
df.loc[(df['age'] >= 20) & (df['age']<= 39), 'ageGroup'] = 'Adult'
df.loc[(df['age'] >= 40) & (df['age'] <= 59), 'ageGroup'] = 'Middle Age Adult'
df.loc[(df['age'] > 60), 'ageGroup'] = 'Senior Adult'
df


object_feat = [feature for feature in df1.columns if df1[feature].dtype == "O"]
df1[object_feat].head()


from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()

for i in object_feat:
    df1[i] = df1[[i]].apply(le.fit_transform)


df1.head()


from sklearn.preprocessing import StandardScaler
scaler = StandardScaler() 
df2 = scaler.fit_transform(df1)


df2 = pd.DataFrame(df2,columns = df1.columns)

df1


scaled_df= df2.copy()

df2


from sklearn.cluster import KMeans
from yellowbrick.cluster import KElbowVisualizer
model = KMeans()
visualizer = KElbowVisualizer(model, k=(1,11))
visualizer.fit(scaled_df)
visualizer.show();


kmeans = KMeans(n_clusters=3,init='k-means++',random_state=0)
pred_kmeans = kmeans.fit_predict(scaled_df)


df['pred_kmeans'] = pred_kmeans+1
df.head()


df.isnull().sum()

education            0
marital_status       0
income               0
recency              0
mntwines             0
mntfruits            0
mntmeatproducts      0
mntfishproducts      0
mntsweetproducts     0
mntgoldprods         0
total_kids           0
total_accep          0
total_purchase       0
age                  0
isparent             0
spent                0
total_days           0
family               0
ageGroup            49
pred_kmeans          0
dtype: int64


df1['pred_kmeans'] = pred_kmeans+1
df1.head()


personal = df[['education','marital_status','total_kids','total_accep','family','isparent']]


fig,ax = plt.subplots(2,3,figsize=(20,10))
sns.set_style('darkgrid')
for variable, subplot in zip(personal,ax.flatten()):
    sns.countplot(df[variable],hue=df['pred_kmeans'],ax=subplot,palette='cool')


other = df[['income','total_purchase','age','spent','total_days','recency']]


fig,ax = plt.subplots(2,3,figsize=(20,10))
sns.set_style('darkgrid')
for variable, subplot in zip(other,ax.flatten()):
    sns.kdeplot(data= df,x=variable,hue='pred_kmeans',ax=subplot,palette='cool')


plt.figure(figsize=(20,10))
sns.scatterplot(data=df, x='income', y='spent', hue='pred_kmeans' ,palette='cool');
plt.xlabel('Income', fontsize=20, labelpad=20)
plt.ylabel('Total Spendings', fontsize=20, labelpad=20);


cluster_spendings = df.groupby('pred_kmeans')[['mntwines', 'mntfruits','mntmeatproducts', 
                                                  'mntfishproducts', 'mntsweetproducts', 'mntgoldprods']].sum()

cluster_spendings.plot(kind='bar', stacked=True, figsize=(9,7), color=['#dc4c4c','#e17070','#157394','#589cb4','#bcb4ac','#3c444c'])

plt.title('Spending Habits by Cluster')
plt.xlabel('Clusters', fontsize=20, labelpad=20)
plt.ylabel('Spendings', fontsize=20, labelpad=20);
plt.xticks(rotation=0, ha='center');


fig = px.scatter(df1, x="total_purchase", y="income", color="pred_kmeans",
    size="spent", size_max=45, log_x=True)

fig.update_layout(legend=dict(
    yanchor="top",
    y=0.99,
    xanchor="left",
    x=0.01
))

fig.show()


for i in other:
    plt.figure()
    sns.jointplot(x=df[i], y=df["spent"], hue =df["pred_kmeans"], kind="kde")
    plt.show()

---------------------------------------------------------------------------
LinAlgError                               Traceback (most recent call last)
<ipython-input-99-aa2cc0deb072> in <module>
      1 for i in other:
      2     plt.figure()
----> 3     sns.jointplot(x=df[i], y=df["spent"], hue =df["pred_kmeans"], kind="kde")
      4     plt.show()

~\anaconda3\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~\anaconda3\lib\site-packages\seaborn\axisgrid.py in jointplot(x, y, data, kind, color, height, ratio, space, dropna, xlim, ylim, marginal_ticks, joint_kws, marginal_kws, hue, palette, hue_order, hue_norm, **kwargs)
   2106 
   2107         joint_kws.setdefault("color", color)
-> 2108         grid.plot_joint(kdeplot, **joint_kws)
   2109 
   2110         marginal_kws.setdefault("color", color)

~\anaconda3\lib\site-packages\seaborn\axisgrid.py in plot_joint(self, func, **kwargs)
   1690 
   1691         if str(func.__module__).startswith("seaborn"):
-> 1692             func(x=self.x, y=self.y, **kwargs)
   1693         else:
   1694             func(self.x, self.y, **kwargs)

~\anaconda3\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~\anaconda3\lib\site-packages\seaborn\distributions.py in kdeplot(x, y, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, weights, hue, palette, hue_order, hue_norm, multiple, common_norm, common_grid, levels, thresh, bw_method, bw_adjust, log_scale, color, fill, data, data2, **kwargs)
   1740     else:
   1741 
-> 1742         p.plot_bivariate_density(
   1743             common_norm=common_norm,
   1744             fill=fill,

~\anaconda3\lib\site-packages\seaborn\distributions.py in plot_bivariate_density(self, common_norm, fill, levels, thresh, color, legend, cbar, cbar_ax, cbar_kws, estimate_kws, **contour_kws)
   1080             # Estimate the density of observations at this level
   1081             observations = observations["x"], observations["y"]
-> 1082             density, support = estimator(*observations, weights=weights)
   1083 
   1084             # Transform the support grid back to the original scale

~\anaconda3\lib\site-packages\seaborn\_statistics.py in __call__(self, x1, x2, weights)
    187             return self._eval_univariate(x1, weights)
    188         else:
--> 189             return self._eval_bivariate(x1, x2, weights)
    190 
    191 

~\anaconda3\lib\site-packages\seaborn\_statistics.py in _eval_bivariate(self, x1, x2, weights)
    162         support = self.support
    163         if support is None:
--> 164             support = self.define_support(x1, x2, cache=False)
    165 
    166         kde = self._fit([x1, x2], weights)

~\anaconda3\lib\site-packages\seaborn\_statistics.py in define_support(self, x1, x2, weights, cache)
    119             support = self._define_support_univariate(x1, weights)
    120         else:
--> 121             support = self._define_support_bivariate(x1, x2, weights)
    122 
    123         if cache:

~\anaconda3\lib\site-packages\seaborn\_statistics.py in _define_support_bivariate(self, x1, x2, weights)
    102             clip = (clip, clip)
    103 
--> 104         kde = self._fit([x1, x2], weights)
    105         bw = np.sqrt(np.diag(kde.covariance).squeeze())
    106 

~\anaconda3\lib\site-packages\seaborn\_statistics.py in _fit(self, fit_data, weights)
    135             fit_kws["weights"] = weights
    136 
--> 137         kde = stats.gaussian_kde(fit_data, **fit_kws)
    138         kde.set_bandwidth(kde.factor * self.bw_adjust)
    139 

~\anaconda3\lib\site-packages\scipy\stats\kde.py in __init__(self, dataset, bw_method, weights)
    204             self._neff = 1/sum(self._weights**2)
    205 
--> 206         self.set_bandwidth(bw_method=bw_method)
    207 
    208     def evaluate(self, points):

~\anaconda3\lib\site-packages\scipy\stats\kde.py in set_bandwidth(self, bw_method)
    554             raise ValueError(msg)
    555 
--> 556         self._compute_covariance()
    557 
    558     def _compute_covariance(self):

~\anaconda3\lib\site-packages\scipy\stats\kde.py in _compute_covariance(self)
    566                                                bias=False,
    567                                                aweights=self.weights))
--> 568             self._data_inv_cov = linalg.inv(self._data_covariance)
    569 
    570         self.covariance = self._data_covariance * self.factor**2

~\anaconda3\lib\site-packages\scipy\linalg\basic.py in inv(a, overwrite_a, check_finite)
    975         inv_a, info = getri(lu, piv, lwork=lwork, overwrite_lu=1)
    976     if info > 0:
--> 977         raise LinAlgError("singular matrix")
    978     if info < 0:
    979         raise ValueError('illegal value in %d-th argument of internal '

LinAlgError: singular matrix

<Figure size 576x396 with 0 Axes>

<Figure size 576x396 with 0 Axes>

<Figure size 576x396 with 0 Axes>

<Figure size 576x396 with 0 Axes>


sns.kdeplot(df.income,df.spent)

<AxesSubplot:xlabel='income', ylabel='spent'>


sns.kdeplot(df.recency,df.spent,hue =df["pred_kmeans"])

<AxesSubplot:xlabel='recency', ylabel='spent'>


sns.scatterplot(df.recency,df.total_purchase,hue =df["pred_kmeans"],palette='cool')

<AxesSubplot:xlabel='recency', ylabel='total_purchase'>


sns.scatterplot(df.recency,df.spent,hue =df["pred_kmeans"],palette='cool')

<AxesSubplot:xlabel='recency', ylabel='spent'>


sns.scatterplot(df.recency,df.income,hue =df["pred_kmeans"],palette='cool')

<AxesSubplot:xlabel='recency', ylabel='income'>


palette='cool'
plt.figure(figsize=(20,10))
sns.scatterplot(data=df, x='pred_kmeans', y='spent', hue='pred_kmeans' ,palette='cool');
plt.xlabel('Income', fontsize=20, labelpad=20)
plt.ylabel('Total Spendings', fontsize=20, labelpad=20);

	ID	Year_Birth	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	...	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Z_CostContact	Z_Revenue	Response
count	2240.000000	2240.000000	2216.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	...	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.0	2240.0	2240.000000
mean	5592.159821	1968.805804	52247.251354	0.444196	0.506250	49.109375	303.935714	26.302232	166.950000	37.525446	...	5.316518	0.072768	0.074554	0.072768	0.064286	0.013393	0.009375	3.0	11.0	0.149107
std	3246.662198	11.984069	25173.076661	0.538398	0.544538	28.962453	336.597393	39.773434	225.715373	54.628979	...	2.426645	0.259813	0.262728	0.259813	0.245316	0.114976	0.096391	0.0	0.0	0.356274
min	0.000000	1893.000000	1730.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
25%	2828.250000	1959.000000	35303.000000	0.000000	0.000000	24.000000	23.750000	1.000000	16.000000	3.000000	...	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
50%	5458.500000	1970.000000	51381.500000	0.000000	0.000000	49.000000	173.500000	8.000000	67.000000	12.000000	...	6.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
75%	8427.750000	1977.000000	68522.000000	1.000000	1.000000	74.000000	504.250000	33.000000	232.000000	50.000000	...	7.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
max	11191.000000	1996.000000	666666.000000	2.000000	2.000000	99.000000	1493.000000	199.000000	1725.000000	259.000000	...	20.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	3.0	11.0	1.000000

	education	marital_status	income	total_kids	total_accep	total_purchase	age	isparent	spent
0	-0.893586	1.349603	0.287105	-1.264598	0.617244	1.317945	1.018352	-1.581139	1.676245
1	-0.893586	1.349603	-0.260882	1.404572	-0.502808	-1.159273	1.274785	0.632456	-0.963297
2	-0.893586	-0.740959	0.913196	-1.264598	-0.502808	0.796425	0.334530	-1.581139	0.280110
3	-0.893586	-0.740959	-1.176114	0.069987	-0.502808	-0.898513	-1.289547	0.632456	-0.920135
4	0.571657	-0.740959	0.294307	0.069987	-0.502808	0.535666	-1.033114	0.632456	-0.307562
...	...	...	...	...	...	...	...	...	...
2207	-0.893586	-0.740959	0.430444	0.069987	-0.502808	0.405286	0.163575	0.632456	1.218061
2208	0.571657	-0.740959	0.560123	2.739158	0.617244	0.926805	1.958607	0.632456	-0.271040
2209	-0.893586	1.349603	0.233347	-1.264598	0.617244	0.535666	-1.033114	-1.581139	1.052052
2210	0.571657	-0.740959	0.803172	0.069987	-0.502808	1.057185	1.103830	0.632456	0.391336
2211	0.571657	-0.740959	0.042290	1.404572	0.617244	-0.507373	1.274785	0.632456	-0.722584

VISUALIZATION univarient¶

for numerical values¶

solving outlier¶

bivarient¶

MODEL BUILDING¶

FINAL ANALYSIS¶

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	...	NumWebVisitsMonth	Z_CostContact	Z_Revenue	Response
0	5524	1957	Graduation	Single	58138.0	0	0	2012-09-04	58	635	...	7	3	11	1
1	2174	1954	Graduation	Single	46344.0	1	1	2014-03-08	38	11	...	5	3	11	0
2	4141	1965	Graduation	Together	71613.0	0	0	2013-08-21	26	426	...	4	3	11	0
3	6182	1984	Graduation	Together	26646.0	1	0	2014-02-10	26	11	...	6	3	11	0
4	5324	1981	PhD	Married	58293.0	1	0	2014-01-19	94	173	...	5	3	11	0

	education	marital_status	income	dt_customer	recency	mntwines	mntfruits	mntmeatproducts	mntfishproducts	mntsweetproducts	mntgoldprods	total_kids	total_accep	total_purchase	age	isparent
0	Graduate	Single	58138.0	2012-09-04	58	635	88	546	172	88	88	0	1	25	57	False
1	Graduate	Single	46344.0	2014-03-08	38	11	1	6	2	1	6	2	0	6	60	True
2	Graduate	Married	71613.0	2013-08-21	26	426	49	127	111	21	42	0	0	21	49	False
3	Graduate	Married	26646.0	2014-02-10	26	11	4	20	10	3	5	1	0	8	30	True
4	Postgraduate	Married	58293.0	2014-01-19	94	173	43	118	46	27	15	1	0	19	33	True

	education	marital_status	income	dt_customer	recency	mntwines	mntfruits	mntmeatproducts	mntfishproducts	mntsweetproducts	mntgoldprods	total_kids	total_accep	total_purchase	age	isparent	spent	total_days	family
192	Undergraduate	Single	36640.0	2013-09-26	99	15	6	8	7	4	25	1	0	6	114	True	65	276	2
239	Undergraduate	Single	60182.0	2014-05-17	23	8	0	5	7	0	2	1	0	4	121	True	22	43	2
339	Postgraduate	Married	83532.0	2013-09-26	36	755	144	562	104	64	224	0	1	15	115	False	1853	276	2

	education	marital_status	income	total_kids	total_accep	total_purchase	age	isparent	spent
0	0	1	58138.0	0	1	25	57	False	1617
1	0	1	46344.0	2	0	6	60	True	27
2	0	0	71613.0	0	0	21	49	False	776
3	0	0	26646.0	1	0	8	30	True	53
4	1	0	58293.0	1	0	19	33	True	422